神经网络中的隐状态：从模型权重中恢复漂移数据的时间结构

中文摘要 AI 辅助

时间漂移的数据流可能会经历离散的状态，而非连续变化。我们探究是否能通过对模型权重的时间有序轨迹拟合隐马尔可夫模型（HMM），从在该流上训练的模型权重中恢复这些状态。我们在两个已知会随时间漂移的领域研究该问题：使用Fakeddit数据集的多模态虚假信息检测，以及使用Yelp数据集的情感分析。我们在连续时间窗口上训练分类器，并对其对齐后的权重轨迹拟合HMM，以恢复将每个时间线划分为连贯阶段的隐状态。在两个数据集上，与跨状态边界的窗口相比，分类器对与训练窗口状态相同的窗口数据泛化效果更好。这种状态内迁移优势在控制时间邻近性后仍然存在，且适度超过了将连续状态划分为相等大小的朴素划分所带来的优势。尽管这些状态仅从模型权重中估计，但它们与数据类分布的变化比与用于估计它们的权重空间几何的相关性更强。在对类别差异和滞后进行残差化后，两个任务的状态内优势均超过了其排列零假设，表明这些状态恢复了与数据分布之外的迁移相关的结构。所有效应在两个任务上均能复制，但在Yelp数据集上有所减弱，其标签分布在时间上更稳定。

英文摘要

A temporally drifting data stream may pass through discrete regimes rather than changing continuously. We ask whether such regimes are recoverable from the weights of models trained on the stream, using a hidden Markov model (HMM) fit to the chronologically ordered trajectory of those weights. We study this question in two domains known to drift over time: multimodal misinformation detection, using the Fakeddit dataset; and sentiment analysis, using the Yelp dataset. We train classifiers on consecutive temporal windows and fit an HMM to the trajectory of their aligned weights, recovering latent states that partition each timeline into coherent phases. On both datasets, classifiers generalize better to data from windows sharing the state of their training window than to windows across state boundaries. This within-state transfer advantage survives a control for temporal proximity and modestly exceeds the advantage recovered by a naive partition into contiguous states of equal size. Although the states are estimated solely from model weights, they correlate more strongly with shifts in the data's class distribution than with the weight-space geometry used to estimate them. After class divergence and lag are residualized out, the within-state advantage exceeds its permutation null on both tasks, indicating that the states recover structure relevant to transfer beyond the data distribution. Every effect replicates on both tasks but is attenuated on Yelp, whose label distribution is more temporally stable.

发表机构

• Princeton University（普林斯顿大学）

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